//Study 1 Analysis

**Control condition**
revrs control_q1 control_q2 control_q3 control_q4 control_q5 control_q6

label define scale 1 "Strongly Disagree" 2 "Disgree" 3 "Somewhat Disgree" 4 "Neither Agree nor Disagree" 5 "Somewhat Agree" 6 "Agree" 7 "Strongly Agree"

label variable revcontrol_q1 "control_item1_rev"
label values revcontrol_q1 scale
tab revcontrol_q1

label variable revcontrol_q2 "control_item2_rev"
label values revcontrol_q2 scale
tab revcontrol_q2

label variable revcontrol_q3 "control_item3_rev"
label values revcontrol_q3 scale
tab revcontrol_q3

label variable revcontrol_q4 "control_item4_rev"
label values revcontrol_q4 scale
tab revcontrol_q4

label variable revcontrol_q5 "control_item5_rev"
label values revcontrol_q5 scale
tab revcontrol_q5

label variable revcontrol_q6 "control_item6_rev"
label values revcontrol_q6 scale
tab revcontrol_q6

gen control_total = revcontrol_q1 + revcontrol_q2 + revcontrol_q3 + revcontrol_q4 + revcontrol_q5 + revcontrol_q6
tab control_total

gen control_avg = (control_total/6)
tab control_avg

gen control_score = ((control_avg-1)/6)
tab control_score

**Know a Lot Condition**
revrs knowlot_q1 knowlot_q2 knowlot_q3 knowlot_q4 knowlot_q5 knowlot_q6

label variable revknowlot_q1 "knowlot_item1_rev"
label values revknowlot_q1 scale
tab revknowlot_q1

label variable revknowlot_q2 "knowlot_item2_rev"
label values revknowlot_q2 scale
tab revknowlot_q2

label variable revknowlot_q3 "knowlot_item3_rev"
label values revknowlot_q3 scale
tab revknowlot_q3

label variable revknowlot_q4 "knowlot_item4_rev"
label values revknowlot_q4 scale
tab revknowlot_q4

label variable revknowlot_q5 "knowlot_item5_rev"
label values revknowlot_q5 scale
tab revknowlot_q5

label variable revknowlot_q6 "knowlot_item6_rev"
label values revknowlot_q6 scale
tab revknowlot_q6

gen knowlot_total = revknowlot_q1 + revknowlot_q2 + revknowlot_q3 + revknowlot_q4 + revknowlot_q5 + revknowlot_q6
tab knowlot_total
recode knowlot_total (134 = .)
recode knowlot_total (138 = .)

gen knowlot_avg = (knowlot_total/6)
tab knowlot_avg

gen knowlot_score = ((knowlot_avg-1)/6)
tab knowlot_score

**Know Little Condition**
revrs knowlittle_q1 knowlittle_q2 knowlittle_q3 knowlittle_q4 knowlittle_q5 knowlittle_q6

label variable revknowlittle_q1 "knowlittle_item1_rev"
label values revknowlittle_q1 scale
tab revknowlittle_q1

label variable revknowlittle_q2 "knowlittle_item2_rev"
label values revknowlittle_q2 scale
tab revknowlittle_q2

label variable revknowlittle_q3 "knowlittle_item3_rev"
label values revknowlittle_q3 scale
tab revknowlittle_q3

label variable revknowlittle_q4 "knowlittle_item4_rev"
label values revknowlittle_q4 scale
tab revknowlittle_q4

label variable revknowlittle_q5 "knowlittle_item5_rev"
label values revknowlittle_q5 scale
tab revknowlittle_q5

label variable revknowlittle_q6 "knowlittle_item6_rev"
label values revknowlittle_q6 scale
tab revknowlittle_q6

gen knowlittle_total = revknowlittle_q1 + revknowlittle_q2 + revknowlittle_q3 + revknowlittle_q4 + revknowlittle_q5 + revknowlittle_q6
tab knowlittle_total

gen knowlittle_avg = (knowlittle_total/6)
tab knowlittle_avg

gen knowlittle_score = ((knowlittle_avg-1)/6)
tab knowlittle_score

**Condition and Cynicism Variables**
gen condition = .
replace condition = 1 if control_score != . 
replace condition = 2 if knowlot_score != . 
replace condition = 3 if knowlittle_score != . 
tab condition

label define conditionl 1 "Control" 2 "Know a Lot" 3 "Know Little" 
label values condition conditionl
tab condition

gen cynicsm = .
replace cynicsm = control_score if condition == 1
replace cynicsm = knowlot_score if condition == 2
replace cynicsm = knowlittle_score if condition == 3
tab cynicsm

**t-tests**
ttest control_score == knowlot_score, unpaired
ttest control_score == knowlittle_score, unpaired
ttest knowlot_score == knowlittle_score, unpaired

**Figure 1**
reg cynicsm i.condition
margins condition
marginsplot
marginsplot, horizontal recast(scatter) yscale (reverse) xlab(0(.1)1) title ("") ytitle("Condition") xtitle("Mean Cynicism Score with 95% Confidence Interval")

**Demographics**
tab Gender
gen man = 0
replace man = 1 if Gender == 1
tab man

tab Race
gen white = 0 
replace white = 1 if Race == "1"
tab white

gen black = 0 
replace black = 1 if Race == "2"
tab black

tab PID
tab PIDlean
gen dem = 0
replace dem = 1 if PID == 1
replace dem = 1 if PIDlean == 2
tab dem

tab Age

tab Educ

gen college = 0
replace college = 1 if Educ == 4 | Educ == 5
tab college

tab PIDstrong
gen strong_partisan = 0
replace strong_partisan = 1 if PIDstrong ==1
tab strong_partisan

**Randomization Check**
reg condition man white black dem, robust

**Table S5.1. Estimates of Political Cynicism with Control Variables, Study 1.**
eststo: quietly reg cynicsm i.condition Age man white Educ dem strong_partisan, robust 
estout, cells(b(star fmt(3)) se(par fmt(2))) stats(N)
eststo clear

**Table S5.2 Estimates of Political Cynicism with Education Interaction, Study 1.**
eststo: quietly reg cynicsm i.condition##c.Educ Age man white dem strong_partisan, robust 
estout, cells(b(star fmt(3)) se(par fmt(2))) stats(N)
eststo clear

**Table S5.3 Estimates of Political Cynicism with Education and Age Interactions, Study 1.**
eststo: quietly reg cynicsm i.condition##c.Educ##c.Age man white dem strong_partisan, robust 
estout, cells(b(star fmt(3)) se(par fmt(2))) stats(N)
eststo clear

//Study 2 Analysis

**Conditions**
label variable P_STYLE "Condition"
label define P_STYLEl 1 "Control" 2 "Fake Good" 3 "Fake Bad" 
label values P_STYLE P_STYLEl
tab P_STYLE

gen control_condition = 1 if P_STYLE == 1 
gen good_condition = 1 if P_STYLE == 2 
gen bad_condition = 1 if P_STYLE == 3 

**Control Group**
gen S1A_r = .
replace S1A_r = 0 if S1A == 2
replace S1A_r = 1 if S1A == 1
tab S1A_r S1A

gen S1B_r = .
replace S1B_r = 0 if S1B == 2
replace S1B_r = 1 if S1B == 1
tab S1B_r S1B

gen S1C_r = .
replace S1C_r = 0 if S1C == 2
replace S1C_r = 1 if S1C == 1
tab S1C_r S1C

gen S1D_r = .
replace S1D_r = 0 if S1D == 2
replace S1D_r = 1 if S1D == 1
tab S1D_r S1D

gen S1E_r = .
replace S1E_r = 0 if S1E == 2
replace S1E_r = 1 if S1E == 1
tab S1E_r S1E

gen S1F_r = .
replace S1F_r = 0 if S1F == 2
replace S1F_r = 1 if S1F == 1
tab S1F_r S1F

gen control_total = S1A_r + S1B_r + S1C_r + S1D_r + S1E_r + S1F_r
tab control_total

gen control_score = (control_total/6)
tab control_score

**Fake Good Condition**
gen S2A_r = .
replace S2A_r = 0 if S2A == 2
replace S2A_r = 1 if S2A == 1
tab S2A_r S2A

gen S2B_r = .
replace S2B_r = 0 if S2B == 2
replace S2B_r = 1 if S2B == 1
tab S2B_r S2B

gen S2C_r = .
replace S2C_r = 0 if S2C == 2
replace S2C_r = 1 if S2C == 1
tab S2C_r S2C

gen S2D_r = .
replace S2D_r = 0 if S2D == 2
replace S2D_r = 1 if S2D == 1
tab S2D_r S2D

gen S2E_r = .
replace S2E_r = 0 if S2E == 2
replace S2E_r = 1 if S2E == 1
tab S2E_r S2E

gen S2F_r = .
replace S2F_r = 0 if S2F == 2
replace S2F_r = 1 if S2F == 1
tab S2F_r S2F

gen good_total = S2A_r + S2B_r + S2C_r + S2D_r + S2E_r + S2F_r
tab good_total

gen good_score = (good_total/6)
tab good_score

**Fake Bad Condition**
gen S3A_r = .
replace S3A_r = 0 if S3A == 2
replace S3A_r = 1 if S3A == 1
tab S3A_r S3A

gen S3B_r = .
replace S3B_r = 0 if S3B == 2
replace S3B_r = 1 if S3B == 1
tab S3B_r S3B

gen S3C_r = .
replace S3C_r = 0 if S3C == 2
replace S3C_r = 1 if S3C == 1
tab S3C_r S3C

gen S3D_r = .
replace S3D_r = 0 if S3D == 2
replace S3D_r = 1 if S3D == 1
tab S3D_r S3D

gen S3E_r = .
replace S3E_r = 0 if S3E == 2
replace S3E_r = 1 if S3E == 1
tab S3E_r S3E

gen S3F_r = .
replace S3F_r = 0 if S3F == 2
replace S3F_r = 1 if S3F == 1
tab S3F_r S3F

gen bad_total = S3A_r + S3B_r + S3C_r + S3D_r + S3E_r + S3F_r
tab bad_total

gen bad_score = (bad_total/6)
tab bad_score

**Cynicism**
gen cynical = .
replace cynical = control_score if P_STYLE == 1 
replace cynical = good_score if P_STYLE == 2
replace cynical = bad_score if P_STYLE == 3
tab cynical P_STYLE

**Demographics**
tab AGE7

gen man = 0
replace man = 1 if GENDER == 1
tab man GENDER

gen woman = 0
replace woman = 1 if GENDER == 2
tab woman

tab INCOME9

tab RACETHNICITY
gen white = 0
replace white = 1 if RACETHNICITY == 1
tab white RACETHNICITY

gen black = 0
replace black = 1 if RACETHNICITY == 2
tab black

tab EDUC5

tab MARITAL

tab EMPLOY

tab INCOME9

tab PartyID5
gen dem = 0
replace dem = 1 if PartyID5 == 1 
replace dem = 1 if PartyID5 == 2
tab dem

tab PartyID7
gen strong_partisan = 0
replace strong_partisan = 1 if PartyID7 == 1
replace strong_partisan = 1 if PartyID7 == 7
tab strong_partisan PartyID7

**Political Knowledge, Engagement, and Interest**
tab PA008
gen court = 0
replace court = 1 if PA008 == 3
tab court PA008

tab PA012A

tab PA012B
recode PA012B (98=.)

tab PA012C
recode PA012C (98=.)

tab PA012D
recode PA012D (98=.)

tab PA012E
recode PA012E (98=.)

tab PA012F
recode PA012F (98=.)

tab PA012G
recode PA012G (98=.)

tab PA012H
recode PA012H (98=.)

tab PA012I
recode PA012I (98=.)

tab PA012J
recode PA012J (98=.)

tab PA012K
recode PA012K (98=.)

revrs PA012A PA012B PA012C PA012D PA012E PA012F PA012G PA012H PA012I PA012J PA012K

gen engagement = revPA012A + revPA012B + revPA012C + revPA012D + revPA012E + revPA012F + revPA012G + revPA012H + revPA012I + revPA012J + revPA012K
tab engagement
gen engagement_score1 = engagement/11
tab engagement_score1
gen engagement_score = (engagement_score1-1)
tab engagement_score
summarize engagement_score

tab PA006
recode PA006 (98=.)
rename PA006 interest

**t-tests**
ttest control_score == good_score, unpaired
ttest control_score == bad_score, unpaired
ttest bad_score == good_score, unpaired

**Figure 2**
reg cynical i.P_STYLE
margins P_STYLE
marginsplot
marginsplot, horizontal recast(scatter) yscale (reverse) xlab(0(.1)1) title ("") ytitle("Condition") xtitle("Mean Cynicism Score with 95% Confidence Interval")

**Randomization Check**
reg P_STYLE man white black dem ideology court engagement_score interest AGE7 employed married INCOME EDUC5

**Table S5.4. Estimates of Political Cynicism with Control Variables, Study 2.**
eststo: quietly reg cynical i.P_STYLE AGE7 man white EDUC5 dem strong_partisan
estout, cells(b(star fmt(3)) se(par fmt(2))) stats(N)
eststo clear

**Table S5.5 Estimates of Political Cynicism with Political Knowledge, Engagement, and Interest, Study 2.**
eststo: quietly reg cynical i.P_STYLE##court AGE7 man white EDUC5 dem strong_partisan
eststo: quietly reg cynical i.P_STYLE##c.engagement_score AGE7 man white EDUC5 dem strong_partisan 
eststo: quietly reg cynical i.P_STYLE##c.interest AGE7 man white EDUC5 dem strong_partisan 
estout, cells(b(star fmt(3)) se(par fmt(2))) stats(N)
eststo clear

**Figure S5.6 Study 2 Cynicism Score by Condition (Weighted).**
set more off 
svyset [pweight=WEIGHT]

svy: reg cynical i.P_STYLE
margins P_STYLE
marginsplot
marginsplot, horizontal recast(scatter) yscale (reverse) xlab(0(.1)1) title ("") ytitle("Condition") xtitle("Mean Cynicism Score with 95% Confidence Interval")

**Table S5.7. Estimates of Political Cynicism with Control Variables, Study 2 (Weighted).**
eststo: quietly svy: reg cynical i.P_STYLE AGE7 man white EDUC5 dem strong_partisan
estout, cells(b(star fmt(3)) se(par fmt(2))) stats(N)
eststo clear

**Table S5.6 Estimates of Political Cynicism with Political Knowledge, Engagement, and Interest, Study 2 (Weighted).**
eststo: quietly svy: reg cynical i.P_STYLE##court AGE7 man white EDUC5 dem strong_partisan
eststo: quietly svy: reg cynical i.P_STYLE##c.engagement_score AGE7 man white EDUC5 dem strong_partisan 
eststo: quietly svy: reg cynical i.P_STYLE##c.interest AGE7 man white EDUC5 dem strong_partisan 
estout, cells(b(star fmt(3)) se(par fmt(2))) stats(N)
eststo clear

//Study 3 Analysis

**Control**
revrs control_1 control_2 control_3 control_4 control_5 control_6

gen control_total = revcontrol_1 + revcontrol_2 + revcontrol_3 + revcontrol_4 + revcontrol_5 + revcontrol_6
tab control_total

gen control_score1 = (control_total/6)
tab control_score1

gen control_score = ((control_score1 - 1)/6)
tab control_score

**Know a Lot**
revrs Hillary_know_lot_1 Hillary_know_lot_2 Hillary_know_lot_3 Hillary_know_lot_4 Hillary_know_lot_5 Hillary_know_lot_6

gen knowlot_total = revHillary_know_lot_1 + revHillary_know_lot_2 + revHillary_know_lot_3 + revHillary_know_lot_4 + revHillary_know_lot_5 + revHillary_know_lot_6
tab knowlot_total

gen knowlot_score1 = (knowlot_total/6)
tab knowlot_score1

gen knowlot_score = ((knowlot_score1 - 1)/6)
tab knowlot_score

**Know Little**
revrs HillaryKnowLittle_1 HillaryKnowLittle_2 HillaryKnowLittle_3 HillaryKnowLittle_4 HillaryKnowLittle_5 HillaryKnowLittle_6

gen knowlittle_total = revHillaryKnowLittle_1 + revHillaryKnowLittle_2 + revHillaryKnowLittle_3 + revHillaryKnowLittle_4 + revHillaryKnowLittle_5 + revHillaryKnowLittle_6
tab knowlittle_total

gen knowlittle_score1 = (knowlittle_total/6)
tab knowlittle_score1

gen knowlittle_score = ((knowlittle_score1 - 1)/6)
tab knowlittle_score

**Fake Bad**
revrs HillaryFakeBad_1 HillaryFakeBad_2 HillaryFakeBad_3 HillaryFakeBad_4 HillaryFakeBad_5 HillaryFakeBad_6

gen bad_total = revHillaryFakeBad_1 + revHillaryFakeBad_2 + revHillaryFakeBad_3 + revHillaryFakeBad_4 + revHillaryFakeBad_5 + revHillaryFakeBad_6
tab bad_total
recode bad_total (129 = .)
recode bad_total (536 = .)
tab bad_total

gen bad_score1 = (bad_total/6)
tab bad_score1

gen bad_score = ((bad_score1 - 1)/6)
tab bad_score

**Fake Good**
revrs HillaryFakeGood_1 HillaryFakeGood_2 HillaryFakeGood_3 HillaryFakeGood_4 HillaryFakeGood_5 HillaryFakeGood_6

gen good_total = revHillaryFakeGood_1 + revHillaryFakeGood_2 + revHillaryFakeGood_3 + revHillaryFakeGood_4 + revHillaryFakeGood_5 + revHillaryFakeGood_6
tab good_total

gen good_score1 = (good_total/6)
tab good_score1

gen good_score = ((good_score1 - 1)/6)
tab good_score

**Conditions**
gen condition = .
replace condition = 1 if FL_14_DO == "Hillarycontrol"
replace condition = 2 if FL_14_DO == "HillaryKnowLot"
replace condition = 3 if FL_14_DO == "HillaryKnowLittle¬†"
replace condition = 4 if FL_14_DO == "HillaryFakeGood¬†"
replace condition = 5 if FL_14_DO == "HillaryFakeBad"
tab condition

label define conditionl 1 "Control" 2 "Know a Lot" 3 "Know Little" 4 "Fake Good" 5 "Fake Bad"  
label values condition conditionl
tab condition

gen cynicisim = .
replace cynicisim = control_score if condition == 1
replace cynicisim = knowlot_score if condition == 2
replace cynicisim = knowlittle_score if condition == 3
replace cynicisim = good_score if condition == 4
replace cynicisim = bad_score if condition == 5
tab cynicisim

**Demographics**
tab educ
gen college = 0
replace college = 1 if educ == 5 | educ == 6 | educ == 7
tab college

tab gender
gen man = 0
replace man = 1 if gender == 1
tab man

tab gender
gen woman = 0
replace woman = 1 if gender == 2
tab woman

tab age
recode age (-99=.)
tab age

gen age_cat = .
replace age_cat = 1 if age > 17 & age < 25
replace age_cat = 2 if age > 24 & age < 35
replace age_cat = 3 if age > 34 & age < 45
replace age_cat = 4 if age > 44 & age < 55
replace age_cat = 5 if age > 54 & age < 65
replace age_cat = 6 if age > 64
tab age_cat age
tab age_cat

tab educ

tab race
gen white = 0
replace white = 1 if race == 5
tab white

gen black = 0
replace black = 1 if race == 2
tab black

**Randomization check**
reg condition man white black educ

**Demographics**
tab woman
tab age_cat
tab white
tab black
tab educ

**T-tests**
mean control_score
mean knowlot_score
mean knowlittle_score
mean bad_score
mean good_score

ttest control_score == knowlot_score, unpaired 
ttest control_score == knowlittle_score, unpaired 
ttest control_score == bad_score, unpaired 
ttest control_score == good_score, unpaired 

ttest knowlot_score == knowlittle_score, unpaired 
ttest knowlot_score == bad_score, unpaired
ttest knowlot_score == good_score, unpaired 

ttest knowlittle_score == bad_score, unpaired 
ttest knowlittle_score == good_score, unpaired

ttest good_score == bad_score, unpaired

**Figure 3**
reg cynicisim i.condition
margins condition
marginsplot, horizontal recast(scatter) yscale (reverse) xlab(0(.1)1) title ("") ytitle("Condition") xtitle("Mean Cynicism Score with 95% Confidence Interval")

**Table S5.9. Estimates of Political Cynicism with Control Variables, Study 3.**
eststo: quietly reg cynicisim i.condition age man white educ, robust 
estout, cells(b(star fmt(3)) se(par fmt(2))) stats(N)
eststo clear

**Table S5.10 Estimates of Political Cynicism with Education Interaction, Study 3.**
eststo: quietly reg cynicisim i.condition##c.educ age man white, robust
estout, cells(b(star fmt(3)) se(par fmt(2))) stats(N)
eststo clear

**Table S5.11 Estimates of Political Cynicism with Education and Age Interactions, Study 3.**
eststo: quietly reg cynicisim i.condition##c.educ##c.age man white, robust
estout, cells(b(star fmt(3)) se(par fmt(2))) stats(N)
eststo clear

**Figure S5.12. Study 3 Cynicism Score by Condition with Full Sample.**
reg cynicisim i.condition
margins condition
marginsplot, horizontal recast(scatter) yscale (reverse) xlab(0(.1)1) title ("") ytitle("Condition") xtitle("Mean Cynicism Score with 95% Confidence Interval")

**Table S5.12. Estimates of Political Cynicism with Control Variables, Study 3 Full Sample.**
eststo: quietly reg cynicisim i.condition age man white educ, robust 
estout, cells(b(star fmt(3)) se(par fmt(2))) stats(N)
eststo clear









